航空发动机气路静电传感器优化设计与安装
Combinational Forecast Method Based on PSO-LSSVM in Spectrometric Oil Analysis of the Aircraft Engine
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摘要: 航空发动机气路静电传感器的结构参数直接影响着传感器的性能。采用有限元的方法,分析了传感器的结构参数对静电传感器特性的影响规律。分析结果表明:增加传感器的感应探极,传感器的灵敏度有所提高,灵敏度分布空间也增大;随着绝缘材料的介电常数增加,灵敏度值有所增加;而绝缘材料厚度的增加将会导致灵敏度下降,且随着轴向位置增加,绝缘层厚度对灵敏度值的影响逐渐减小。对于给定结构参数的传感器,采用数据拟合的方法,获得传感器的灵敏度分布函数,基于拟合系数的变化特点来确定传感器的安装布局;最后通过模拟实验对本文的方法进行了有效性验证。Abstract: The spectrometric oil analysis (SOA) is an important technique for aircraft engine state monitoring andfault diagnosis,and forecasting aircraft engine state through SOA results has an advantage of finding out aircraft en-gine wear fault early. According to the characteristics of the SOA data,the combinational forecast model was set upbased on the least squares support vector machine after Auto Regressive (AR) model,GM(1, 1) model and backpropagation(BP) neural network model. In addition,the particle swarm algorithm was used to optimize the regular-ization parameter of least squares support vector machines(LSSVM) and the parameter of kernel function. Finally,two time series of SOA data were used to verify this model. By comparying with the foundation models,the result ofcombinational forecasting model shows better effect and higher precision of forecast by using the non-linear variableweight and the least squares support vector machines with particle swarm optimization.
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